• Journal of Korean Philosophical Society
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• no.114
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• pp.305-335
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• 2016

In the eighteenth century, the scottish philosophers Francis Hutcheson, David Hume and Adam Smith share the idea that morality comes from moral sense, which is a feeling of approval or disapproval of agent's motive and action. However, they have the different views in explaining the mechanism that generates the moral sentiments. Hutcheson takes a moral sense to be a unique mental faculty that is innate to all humans, and regards it as being guaranteed by supernatural apparatus like divine Providence. Hume and Smith reject Hutcheson's concept of internal moral sense and take a stage further Hutcheson's projects of internalisation by naturalizing morality in terms of the principle of sympathy. It is widely held that Hume's moral sentimentalism is essentially similar to Adam Smith's. Though there are important points of contact between Smith's account of sympathy and Hume's, the differences are considerable. The chief of them lies in the fact that Hume grounds our approval of virtue on our recognition of its utility and convention, and Smith does not. Smith grounds our approval of virtue on the impartial spectator's judgment, i.e., conscience. Hence for Smith, the impartial spectator is the one that bridges the gap between particularity and universality and works the vehicle of practical reason. Given this, in this paper, first, I will clarify the difference between Hume's and Adam Smith's understandings of sympathy. Second, I will elucidate how they explain the process to produce the moral sentiments based on their understandings of sympathy. I shall finally explicate in what way Hume's and Smith's theories on sympathy work as moral normativity.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.947-960
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• 2021

In this study, we examine the AI ethics perception of university students to explore the direction of AI ethics education. For this, 83 students wrote their thoughts about 5 discussion topics on online bulletin board. We analyzed it using language networks, one of the text mining techniques. As a result, 62.5% of students spoke the future of the AI society positively. Second, if there is a self-driving car accident, 39.2% of students thought it is the vehicle owner's responsibility at the current level of autonomous driving. Third, invasion of privacy, abuse of technology, and unbalanced information acquisition were cited as dysfunctions of the development of AI. It was mentioned that ethical education for both AI users and developers is required as a way to minimize malfunctions, and institutional preparations should be carried out in parallel. Fourth, only 19.2% of students showed a positive opinion about a society where face recognition technology is universal. Finally, there was a common opinion that when collecting data including personal information, only the part with the consent should be used. Regarding the use of AI without moral standards, they emphasized the ethical literacy of both users and developers. This study is meaningful in that it provides information necessary to design the contents of artificial intelligence ethics education in liberal arts education.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.95-104
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• 2022

This study aims to find out the influence of the intolerance of uncertainty on entrepreneurial intention by focusing on the start-up support system awareness. While most of existing studies focused on the positive psychological variables and individual environmental characteristics influencing on the entrepreneurial intention, this study has taken a look at the influence of the intolerance of uncertainty as the psychological variable to the entrepreneurial intention to negatively act on the entrepreneurial intention. As a result of the analysis, the intolerance of uncertainty under the control of entrepreneurship and gender is shown to have the negative (-) influence on the entrepreneurial intention and has high level of recognition on the start-up support system perception, and the start-up support system perception is confirmed to have positive (+) influence on the entrepreneurial intention. And, it also indicates that, with respect to the influence of the intolerance of uncertainty on the entrepreneurial intention, the start-up support system perception has the partial medium effect. Following this result of the study, it provides following indications: First the existing studies on the entrepreneurial intention had not covered the intolerance of uncertainty but it is confirmed as the psychological variable with negative influence on the entrepreneurial intention. Second, it is feasible for the preliminary start-up businesses may turn the fear on start-up failure into positive entrepreneurial intention with the start-up support system perception as the leading vehicle. And, third, based on the result, the government should enhance the start-up support system perception even more by seeking ways of efficient publicity to enable more preliminary start-up businesses to participate in diverse start-up support policies. Lastly, it discusses the limitations of this study as well as proposal for ensuing study plans.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.111-121
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• 2024

Recently, thanks to the development of artificial intelligence technologies such as image recognition, research on unmanned aerial vehicles is being actively conducted. In particular, related research is increasing in the field of military drones, which costs a lot to foster professional pilot personnel, and one of them is the study of an intelligent framework for autonomous mission performance of reconnaissance drones. In this study, we tried to design an intelligent framework for unmanned aerial vehicles using the methodology of designing an intelligent framework for service robots. For the autonomous mission performance of unmanned aerial vehicles, the intelligent framework and unmanned aerial vehicle module must be smoothly linked. However, it was difficult to provide interworking for drones using periodic message protocols with model-based interfaces of intelligent frameworks for existing service robots. First, the message model lacked expressive power for periodic message protocols, followed by the problem that interoperability of asynchronous data exchange methods of periodic message protocols and intelligent frameworks was not provided. To solve this problem, this paper proposes a message model extension method for message periodic description to secure the model's expressive power for the periodic message model, and proposes periodic and asynchronous data exchange methods using the extended model to provide interoperability of different data exchange methods.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.1
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• pp.39-47
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• 2024

In this study, LiDAR-detective black material is synthesized by recycling silicon sludge (SS) that is generated from semiconductor manufacturing process, and its recognition is confirmed using two types of LiDAR sensors (MEMS and Rotating LiDAR). In detail, metal impurities on the surface of SS is removed, followed by coating of titanium dioxide (TiO₂) and subsequent chemical reduction to obtain SS-derived black TiO₂ (SS/bTiO₂) material. As-prepared SS/bTiO₂ is mixed with transparent paint to prepare hydrophilic black paints and applied to a glass substrate using a spray gun. SS/bTiO₂-based paint shows similar blackness (L^*=15.7) compared to commercial carbon black-based paint, and remarkable NIR reflectance (26.5R%, 905nm). Furthermore, MEMS and Rotating LiDAR have successfully detected the SS/bTiO₂-based paint. This is attributed to the occurrence of high reflection of light at the interface between the black TiO₂ and the silicon sludge according to the Fresnel's reflection principle. Hence, the new application field to effectively recycle silicon sludge generated in the semiconductor manufacturing process has been presented.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.

• Journal of Animal Environmental Science
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• v.16 no.1
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• pp.1-12
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• 2010

This survey was conducted to investigate the status and strategy of swine manure utilization of 109 swine farms in the Gyeongnam, Korea. The personal properties of owner, types of swine buildings, facilities and equipment for manure management, conditions for manure recycling and farming for recycling resources were surveyed. Age of farm owners were occupied as 44.1% for 50s followed by the 60s with one-forth and 40s with 22.9%. Educational background of farm owners, a high school graduate makes up the largest proportion of farm owners followed by a college graduation with 35.8%. The swine manure collection methods were occupied as 34.9% with totally slurry system and more than 50% slurry system with 34.9% of farms. The manure management cost per ton were occupied as more than two-third with 10,000 won~15,000 won. The cost will pay for manure management, 10,000 won~15,000 won per ton makes up the largest proportion of farm owners. Separator, loader and vehicle to collection, transportation of liquid manure were occupied as 72.5%, 44% and 10.1%, respectively. Recognition of the farming for recycling resources were occupied as 37.6%, however, 25.8% of swine farm owners don't know that. More than sixty percent of swine farms will take a recycling system according to the farming for recycling resources. Conclusively, we have a suggestion in order to promotion of the farming for recycling resources in the Gyeongnam with increasing the portion of recycling of swine manure in each county and revitalizing the marketing of the liquid and solid swine manure fertilizers.